Related papers: Understanding solid nitrogen through machine learn…
We introduce an interatomic potential for hexagonal boron nitride (hBN) based on the Gaussian approximation potential (GAP) machine learning methodology. The potential is based on a training set of configurations collected from density…
We apply to the nucleon-nucleus inelastic process a fully coherent microscopic multiple scattering approach. Our study addresses the complexities inherent in characterizing inelastic scattering events, offering a comprehensive theoretical…
Most widely used machine learned (ML) potentials for condensed phase applications rely on many-body permutationally invariant polynomial (PIP) or atom-centered neural networks (NN). However, these approaches often lack chemical…
The atomic-scale response of inhomogeneous fluids at interfaces and surrounding solute particles plays a critical role in governing chemical, electrochemical and biological processes at such interfaces. Classical molecular dynamics…
Based on deep neural networks (DNNs), deep learning has been successfully applied to many problems, but its mechanism is still not well understood -- especially the reason why over-parametrized DNNs can generalize. A recent statistical…
Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex aqueous systems such as solid-liquid interfaces. Here, we present a machine learning…
Machine-learned force fields have generated significant interest in recent years as a tool for molecular dynamics (MD) simulations, with the aim of developing accurate and efficient models that can replace classical interatomic potentials.…
A combination of state-of-the-art theoretical methods has been used to obtain an atomic-level picture of classical and quantum ordering of protons in cold high-pressure solid hydro-gen. We focus mostly on phases II and III of hydrogen,…
We investigate the statistical behavior of a system comprising fore-aft symmetric rods confined between two vertically vibrating plates using numerical simulations closely resembling the experimental setup studied by Narayan et al., Science…
Past experimental observations of gas-liquid and gas-crystal coexistence in low-salinity suspensions of highly charged colloids have suggested the existence of like charge attraction. Evidence for this phenomenon was also observed in…
In coupled rotor models which describe identical rotating nuclei the nuclear spin states restrict the possible angular momenta of each molecule. There are two mean-field approaches to determining the orientational phase diagrams in such…
Synthetic nanoscale complexes capable of mechanical movement are often studied theoretically using discrete-state models that involve instantaneous transitions between metastable states. A number of general results have been derived within…
While several studies confirmed that machine-learned potentials (MLPs) can provide accurate free energies for determining phase stabilities, the abilities of MLPs for efficiently constructing a full phase diagram of multi-component systems…
We analyzed pattern formation and identified different phases in a system of particles interacting through a non-monotonic short-range repulsive (r<r_c) and long-range attractive (r>r_c) potential, using molecular-dynamics simulations.…
We present a new deep learning-based machine learning potential (MLP) for molecular dynamics simulations of solid carbon monoxide (CO), capable of accurately describing CO vibrations both in the fundamental state and in highly excited…
We analyze the pressure-induced metal-insulator transition in a two-dimensional vertical stack of $H_2$ molecules in x-y plane, and show that it represents a striking example of the Mott-Hubbard-type transition. Our combined exact…
The design of efficient electrolysis devices for pure metal production requires accurate data on the properties of the melts used in the process. This work focuses on two key systems for calcium production: the molten Ca-Cu alloy and the…
We compare the predicted phase behaviour of lead (Pb) using three different interatomic potential models, including an embedded atom method (EAM), a modified embedded atom method (MEAM), and a neural network-based machine-learned model in…
Ab initio simulations of dislocations are essential to build quantitative models of material strength, but the required system sizes are often at or beyond the limit of existing methods. Many important structures are thus missing in the…
The formation of extended sulfur vacancies in MoS2 monolayers is closely associated with catalytic activity and may also be the basis for its memristive behavior. Nanosecond-scale molecular dynamics simulations using machine learning…